Distinction of Fire Source from Smoke Using Discrete Probability Distribution and Neural Networks

Abstract

Fire source distinction is useful in reducing the occurrence of false alarms and in choosing an effective fire extinguishing medium. This study was aimed at distinguishing fire sources through a smoke analysis. For this purpose, a smoke detection chamber was created, which was equipped with one light source and several light sensors for enabling simultaneous detection of light extinction and scattering, respectively. The test fires considered in this study had two kinds of sources: single fire source (paper, wood, and flammable liquid) and mixed fire source (paper-wood, paper-flammable liquid, and wood-flammable liquid mixtures). The amounts of extinction and scattering for each fire were measured experimentally, and the discrete probability distributions were calculated from the measured scattering amount. The optical characteristics of each fire were obtained using extinction data and the calculated probability distributions. These optical characteristics were then used for learning of neural networks, and the learned neural networks were used to distinguish fire sources from smoke generated in the case of both single fires and mixed fires. Results revealed that the neural networks could precisely distinguish fire sources on the basis of the smoke particles in the case of both single fires and mixed fires. The results of this study are expected to be useful in developing an advanced smoke detector that can distinguish fire sources in addition to detecting smoke.